![]() Encouraging the use of this material requires a better understanding of its characteristics and especially its possible pathologies to prevent degradation, costs, and even structural collapse. Structured steel construction is an interesting option to the already established concrete structure at a time when Brazilian civil construction seeks more efficiency. The compressed model also manages to outperform contemporary state-of-the-art models. We empirically show that this type of training compresses the model without sacrificing accuracy despite being up to 10 times smaller than the teacher model. ![]() In this work, we propose an unsupervised training routine to distill the knowledge of complex pre-trained models to lightweight deployment-ready models. Another common issue is the unavailability of labeled data to train complex networks. However, the complexity of defects requires complex and large models making them very difficult to operate on low-memory embedded devices typically used in fabrication labs. Recently, deep learning methods have gained significant traction in mixed-type DPR. Identifying multiple defects in a wafer is generally harder compared to identifying a single defect. During manufacturing, various defects may appear standalone in the wafer or may appear as different combinations. Defect Pattern Recognition (DPR) of wafer maps is crucial for determining the root cause of production defects, which may further provide insight for yield improvement in wafer foundry. Manufacturing wafers is an intricate task involving thousands of steps. We opted for a new model of Ishikawa diagram, resulting from the composition of three fish skeletons corresponding to the main categories of parts accuracy. For each of the three categories of causes there were distributed potential secondary causes on groups of M (man, methods, machines, materials, environment/ medio ambiente-sp.). We took into account the main components of parts precision in the machine construction field. The paper shows the potential causes of the studied problem, which were firstly grouped in three categories, as follows: causes that lead to errors in assessing the dimensional accuracy, causes that determine errors in the evaluation of shape and position abnormalities and causes for errors in roughness evaluation. The most known Ishikawa models are 4M, 5M, 6M, the initials being in order: materials, methods, man, machines, mother nature, measurement. The studied problem was"errors in the evaluation of partsprecision" and this constitutes the head of the Ishikawa diagram skeleton.All the possible, main and secondary causes that could generate the studied problem were identified. The paper presents the results of a study concerning the use of the Ishikawa diagram in analyzing the causes that determine errors in the evaluation of theparts precision in the machine construction field. ![]()
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